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Record W2042985399 · doi:10.1371/journal.pone.0079793

The Southpaw Advantage? - Lateral Preference in Mixed Martial Arts

2013· article· en· W2042985399 on OpenAlexaff
Joseph Baker, Jörg Schorer

Bibliographic record

VenuePLoS ONE · 2013
Typearticle
Languageen
FieldNeuroscience
TopicHemispheric Asymmetry in Neuroscience
Canadian institutionsYork University
Fundersnot available
KeywordsMartial artsLateralityPreferenceElitePsychologyOrientation (vector space)PopulationSocial psychologyDemographyStatisticsDevelopmental psychologyMathematicsVisual artsArtPolitical scienceSociologyGeometry

Abstract

fetched live from OpenAlex

Performers with a left-orientation have a greater likelihood of obtaining elite levels of performance in many interactive sports. This study examined whether combat stance orientation was related to skill and success in Mixed Martial Arts fighters. Data were extracted for 1468 mixed martial artists from a reliable and valid online data source. Measures included fighting stance, win percentage and an ordinal measure of skill based on number of fights. The overall analysis revealed that the fraction of fighters using a southpaw stance was greater than the fraction of left-handers in the general population, but the relationship between stance and hand-preference is not well-understood. Furthermore, t-tests found no statistically significant relationship between laterality and winning percentage, although there was a significant difference between stances for number of fights. Southpaw fighters had a greater number of fights than those using an orthodox stance. These results contribute to an expanding database on the influence of laterality on sport performance and a relatively limited database on variables associated with success in mixed martial arts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.091
GPT teacher head0.245
Teacher spread0.153 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations41
Published2013
Admission routes1
Has abstractyes

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